基于边界延拓改进EMD的断路器机械故障诊断法

Novel Diagnostic Method for Mechanical Fault of High-voltage Circuit Breaker Based on the Improved EMD with Boundary Extension

文中针对经验模态分解(EMD)分解结果的准确性对断路器机械故障诊断结果的影响,提出基于边界延拓的EMD和径向基(RBF)神经网络融合的断路器机械故障诊断方法:首先采用最小二乘法进行边界延拓克服EMD分解过程中的端点效应,以减少信号的拟合误差;然后采用改进的EMD分解法将断路器机械振动信号分解为有限个相互独立的IMF函数,并计算包含不同频率成分的IMF包络的能量熵值,将能量熵向量作为RBF神经网络的输入,再采用交替梯度法训练RBF神经网络模型,实现对断路器机械故障的准确诊断;实验结果说明,该方法可以有效的诊断断路器机械故障类型。

In view of the influence of the accuracy of the empirical mode decomposition （EMD）on mechanical fault diagnosis of high-voltage circuit breaker, a new method combining the improved EMD with boundary extension and the RBF neural network is proposed to improve the mechanical fault diagnosis for high-voltage circuit breaker. The signal boundary is extended with the least squares method to overcome the end-effect in EMD decomposition and to reduce the fitting error of signal. The mechanical vibration signal is decomposed into a finite number of independent IMF functions by the improved EMD method. Then, the energy entropy of each IMF envelope with different frequency is calculated, and the energy entropy vector is taken as the input of RBF neural network. Finally, the RBF neural network model is trained with the alternating gradient method to achieve accurate diagnosis of mechanical fault of circuit breaker. Experimental results show that the proposed method can effectively diagnose mechanical fault type of circuit breakers.